RTU Research Information System
Latviešu English

Publikācija: ANN-Based City Heat Demand Forecast

Publication Type Full-text conference paper published in conference proceedings indexed in SCOPUS or WOS database
Funding for basic activity Unknown
Defending: ,
Publication language English (en)
Title in original language ANN-Based City Heat Demand Forecast
Field of research 2. Engineering and technology
Sub-field of research 2.2 Electrical engineering, Electronic engineering, Information and communication engineering
Authors Kārlis Baltputnis
Romāns Petričenko
Antans Sauļus Sauhats
Keywords Cogeneration, forecasting, market, optimization
Abstract This paper discusses the importance of accurate forecasting tools in solving power system planning, modelling and optimization tasks. While artificial neural networks are widely considered to be one of the best prediction methods, their precision can vary greatly depending on the network structure and parameters. A method of experimentally finding the best ANN parameters has been offered and tested on heat demand forecasting. Some value of the benefits of increased prediction accuracy on the operation of CHP plants has been identified.
DOI: 10.1109/PTC.2017.7981097
Hyperlink: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7981097 
Reference Baltputnis, K., Petričenko, R., Sauhats, A. ANN-Based City Heat Demand Forecast. In: Proceedings of the 12th IEEE PES PowerTech Conference towards and beyond Sustainable Energy Systems, United Kingdom, Manchester, 18-22 June, 2017. Piscataway: IEEE, 2017, pp.1-6. ISBN 978-1-5090-4238-8. e-ISBN 978-1-5090-4237-1. Available from: doi:10.1109/PTC.2017.7981097
Additional information Citation count:
ID 25637